Globally optimal distributed time-varying weight information filter of mobile sensor network-Reference-Cited by-同舟云学术

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Globally optimal distributed time-varying weight information filter of mobile sensor network

Published:2020-07 Issue:11 Volume:357 Page:7308-7326
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Liang Yuan,Dong Xiwang,Han Liang,Li Qingdong,Ren Zhang

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Aeronautical Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference48 articles.

1. Event-triggered minimax state estimation with a relative entropy constraint;Xu;Automatica,2019

2. A new distributed kalman filtering based on means-quare estimation upper bounds;Li;J. Frankl. Inst.,2018

3. Connectivity-preserving flocking for networked lagrange systems with time-varying actuator faults;Feng;Automatica,2019

4. Distributed fault estimation observer design with adjustable parameters for a class of nonlinear interconnected systems;Zhang;IEEE Trans. Cybern.,2019

5. Distributed filtering under false data injection attacks;Yang;Automatica,2019

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1. Towards invariant extended Kalman filter-based resilient distributed state estimation for moving robots over mobile sensor networks under deception attacks;Automatica;2024-01

2. Distributed Cluster Fusion Estimation Under Correlated Noise Conditions;Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control;2022-07-29

3. Centralized fusion estimation over wireless sensor-actuator networks with unobservable packet dropouts;Journal of the Franklin Institute;2022-01

4. Distributed event-triggered target tracking under cyber attacks;Journal of the Franklin Institute;2021-12

5. Recursive distributed fusion estimation for nonlinear stochastic systems with event-triggered feedback;Journal of the Franklin Institute;2021-09

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